Reservoir Computer Optimization for Parity Checking

In recent years, the Reservoir Computing (RC) approach, a recurrent-neural-network-based scheme for Machine Learning (ML), has been used extensively for solving different tasks such as time-series prediction, nonlinear system control and classification tasks. A benchmark problem regarding the latter is the parity check of a random sequence of bits. Although at a first look it seems to be a simple problem, it is known to be a very difficult task to be solved using ML techniques. We shall discuss the reservoir computer hyper-parameters optimization and exploration of different architectures for inputting data to the reservoir to improve the parity classification performance as well as paths toward high-speed hardware implementation.